Update README.md

README.md

@@ -16,11 +16,11 @@ The standard datasets (except ImageNet) used for CLIP-based Prompt Tuning resear
 
 Based on the original datasets, this repository adds **foreground segmentation masks** (generated by [SEEM](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)) of all raw images.
 
-Datasets contain: [ImageNet-1K](https://image-net.org/challenges/LSVRC/2012/index.php), [Caltech101](https://data.caltech.edu/records/mzrjq-6wc02), [Oxford Pets](https://www.robots.ox.ac.uk/~vgg/data/pets/), [StanfordCars](https://ai.stanford.edu/~jkrause/cars/car_dataset.html), [Flowers102](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/), [Food101](https://vision.ee.ethz.ch/datasets_extra/food-101/), [FGVC Aircraft](https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/), [SUN397](http://vision.princeton.edu/projects/2010/SUN/), [DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/), [EuroSAT](https://github.com/phelber/EuroSAT) and [UCF101](https://www.crcv.ucf.edu/data/UCF101.php).
+Datasets contain: [ImageNet](https://image-net.org/challenges/LSVRC/2012/index.php), [Caltech101](https://data.caltech.edu/records/mzrjq-6wc02), [Oxford Pets](https://www.robots.ox.ac.uk/~vgg/data/pets/), [StanfordCars](https://ai.stanford.edu/~jkrause/cars/car_dataset.html), [Flowers102](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/), [Food101](https://vision.ee.ethz.ch/datasets_extra/food-101/), [FGVC Aircraft](https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/), [SUN397](http://vision.princeton.edu/projects/2010/SUN/), [DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/), [EuroSAT](https://github.com/phelber/EuroSAT) and [UCF101](https://www.crcv.ucf.edu/data/UCF101.php).
 
 # Scope of Application
 
-Datasets are uitable for training and improving **foreground-supervised prompt tuning** methods. For example:
+Datasets are suitable for training and improving **foreground-supervised prompt tuning** methods. For example:
 
 - _Decouple before Align: Visual Disentanglement Enhances Prompt Tuning_